Symbols formed by handwriting, when traced on an electronic tablet, are represented by sequences of x-y coordinate pairs. A fundamental unit of handwriting is the stroke. A stroke is considered as a sequence of points, represented by their respective x-y coordinates. Symbols, such as letters of the alphabet and numbers, are assemblages of such strokes.
Desirable features of an automatic handwriting recognition system include an ability to process the input data so as to minimize redundancies, and to also model the data by means of robust statistical techniques. Parameters that are considered are typically dependent on the direction of pen movement. That is, the temporal order of the points is preserved when deriving feature vectors. The rational for preserving the temporal ordering of the stroke data is that there is a degree of consistency in the way characters are formed by a given writer. For example, the letter "O" may be written either in a clockwise or counter-clockwise motion. By allowing for these two possibilities, and retaining the direction of pen movement, it is possible to design a recognition system that is robust with respect to noise because the overall temporal trace of the system is not affected by small fluctuations. Furthermore, it is often possible to distinguish between similar shapes, such as an `S` formed with one stroke and a `5` that is formed with two strokes, by virtue of the number of strokes and/or the direction of pen movement.
However, a handwriting recognition system that is dependent solely upon stroke order may exhibit certain deficiencies. One such deficiency is a difficulty in properly handling delayed strokes, such as the crossing of a `t` and the dotting of an `i` or a `j`. Retraces of characters may also present difficulties. Another deficiency of handwriting recognition systems that process only temporal, or dynamic, input data is an ambiguity that is introduced when modeling multi-stroke characters, such as capital letters. This ambiguity exists because the number of representations of such multi-stroke characters increases geometrically with the number of strokes. Finally, there may be little consistency among different writers in the direction of pen movement. It is therefore necessary to incorporate a large number of character prototypes or templates, to achieve reasonable writer independent recognition performance. However, the use of a large number of templates increases both the memory requirements of the handwriting recognition system and also the processing time that is required to search through the templates to find a most probable match with an input character.
It is an object of this invention to provide a handwriting recognition system that employs both dynamic (temporal) feature vectors and static (spatial) feature vectors when recognizing handwriting.
A further object of this invention is to provide a handwriting recognition method and apparatus that operates in a parallel manner to simultaneously provide both dynamic and static feature vectors in response to input signals from a handwriting transducer.
Another object of this invention is to provide a method and apparatus for deriving static feature vectors that are usable both with on-line, real time handwriting recognition techniques and also with off-line, non-real time techniques, such as in optical character recognition systems.